R package icensmis: Study Design and Data Analysis in the Presence of Error-Prone Diagnostic Tests and Self-Reported Outcomes. We consider studies in which information from error-prone diagnostic tests or self-reports are gathered sequentially to determine the occurrence of a silent event. Using a likelihood-based approach incorporating the proportional hazards assumption, we provide functions to estimate the survival distribution and covariate effects. We also provide functions for power and sample size calculations for this setting.
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References in zbMATH (referenced in 2 articles , 1 standard article )
Showing results 1 to 2 of 2.
- Li, Shuwei; Hu, Tao; Sun, Jianguo: Regression analysis of misclassified current status data (2020)
- Gu, Xiangdong; Ma, Yunsheng; Balasubramanian, Raji: Semiparametric time to event models in the presence of error-prone, self-reported outcomes -- with application to the women’s health initiative (2015)